Simultaneously modeling source code and natural language has many exciting\napplications in automated software development and understanding. Pursuant to\nachieving such technology, we introduce PyMT5, the Python method text-to-text\ntransfer transformer, which is trained to translate between all pairs of Python\nmethod feature combinations: a single model that can both predict whole methods\nfrom natural language documentation strings (docstrings) and summarize code\ninto docstrings of any common style. We present an analysis and modeling effort\nof a large-scale parallel corpus of 26 million Python methods and 7.7 million\nmethod-docstring pairs, demonstrating that for docstring and method generation,\nPyMT5 outperforms similarly-sized auto-regressive language models (GPT2) which\nwere English pre-trained or randomly initialized. On the CodeSearchNet test\nset, our best model predicts 92.1% syntactically correct method bodies,\nachieved a BLEU score of 8.59 for method generation and 16.3 for docstring\ngeneration (summarization), and achieved a ROUGE-L F-score of 24.8 for method\ngeneration and 36.7 for docstring generation.\n